Insulin

Insulin is a vital hormone produced by the beta cells of the pancreas, playing a central role in regulating glucose metabolism throughout the body. Its primary function is to facilitate the uptake of glucose from the bloodstream into cells for energy or storage. This includes converting glucose into glycogen in the liver and muscles, and inhibiting the liver’s production of glucose. Proper insulin function is essential for maintaining stable blood sugar levels and overall metabolic health.

The accurate assessment of insulin levels is biologically fundamental for understanding an individual’s metabolic state. Dysregulation in insulin production or sensitivity can lead to a range of health issues, including insulin resistance and hyperglycemia. Genetic research, particularly genome-wide association studies (GWAS), has increasingly focused on identifying genetic variants that influence insulin levels and related traits^[1]. These studies examine specific insulin-related traits like fasting insulin, 2-hour insulin, HOMA insulin resistance, and the insulinogenic index to uncover the genetic architecture underlying metabolic pathways^[1]. Understanding these genetic influences provides more detailed insights into potentially affected biological pathways ^[1].

Clinically, assessing insulin levels is vital for the diagnosis, monitoring, and management of metabolic disorders such as type 1 and type 2 diabetes, pre-diabetes, and metabolic syndrome. Abnormal insulin levels can indicate impaired glucose regulation, which is a hallmark of these conditions. Genetic studies have shown that insulin-related traits are often analyzed in conjunction with glucose traits to provide a comprehensive view of metabolic health^[2].

From a societal perspective, the rising global prevalence of diabetes and metabolic syndrome underscores the importance of insulin regulation. Insights gained from genetic and metabolic characterization hold the promise of advancing personalized health care and nutrition^[1]. By identifying individuals predisposed to insulin dysregulation through genetic markers, proactive interventions and tailored therapeutic strategies can be developed, potentially mitigating the public health burden of these widespread conditions.

Limitations

Several limitations should be considered when interpreting research on insulin. These pertain to the methodologies employed, the intrinsic complexity of the trait, and the generalizability of findings across diverse populations.

Methodological and Statistical Constraints

Genetic associations with clinical outcomes related to insulin often exhibit small effect sizes, which mandates the screening of very large populations to achieve sufficient statistical power for identifying novel genetic variants. Some genome-wide association studies, for instance, have involved up to 18,000 participants to address this challenge^[1]. The necessity for such extensive cohorts underscores the difficulty in detecting subtle genetic contributions to complex traits like insulin levels, potentially leading to challenges in replicating findings across smaller studies or, conversely, to an inflation of reported effect sizes if statistical rigor is not maintained.

Phenotypic Complexity and Environmental Influences

The accurate characterization of insulin levels is highly susceptible to various physiological and environmental factors, necessitating strict control within study designs. For example, individuals are typically excluded from analyses if their blood samples are non-fasting, if they have diabetes, are on diabetic medication, or are pregnant^[3]. Moreover, robust analyses must account for significant confounders such as age, smoking status, body-mass index, hormone therapy use, and menopausal status^[4]. Inadequate adjustment for these critical variables can introduce considerable noise, obscure genuine genetic signals, and complicate the precise interpretation of genetic associations with insulin levels.

Generalizability Across Diverse Populations

Many genetic studies investigating metabolic traits, including those related to insulin, are conducted within specific cohorts or founder populations, such as those originating from Finland or studies comparing Micronesian and White populations^[3], ^[5]. While these studies offer valuable insights into particular genetic architectures, findings derived from such specific populations may not be universally applicable to other diverse ancestral groups. This specificity can introduce cohort biases, potentially limiting the broader utility of identified genetic loci and hindering a comprehensive understanding of insulin regulation across the global human population.

Remaining Knowledge Gaps and Mechanistic Insights

Current genetic association studies, particularly when focused primarily on clinical outcomes, often provide limited insight into the precise underlying disease-causing mechanisms^[1]. While the field of metabolomics shows promise in detailing affected pathways by measuring intermediate phenotypes on a continuous scale, a substantial portion of the variability in insulin levels remains unexplained by currently identified genetic variants^[1]. Addressing this gap requires deeper integration of genetic, metabolic, and clinical data to fully elucidate insulin biology and progress towards personalized health care and nutrition strategies^[1].

Variants

Genetic variations play a significant role in influencing insulin levels and the risk of related metabolic conditions. These variants can affect genes involved in diverse processes, from insulin secretion and action to immune regulation and glucose metabolism. Understanding these genetic predispositions helps to clarify individual differences in insulin response and susceptibility to conditions like type 2 diabetes.

Several variants in genes crucial for glucose homeostasis and insulin regulation have been identified. For example, variants inTCF7L2 (Transcription Factor 7 Like 2), such as rs7903146 , rs35198068 , and rs4575195 , are strongly associated with an increased risk of type 2 diabetes by impacting pancreatic beta-cell function and insulin secretion. Similarly,beta-cell functionrefers to the capacity of the pancreatic beta cells to produce and secrete insulin, a critical component of glucose homeostasis^[6].

Several conceptual frameworks and operational definitions exist to quantify these complex traits. The Homeostasis Model Assessment (HOMA), specifically HOMA-IR for insulin resistance and HOMA-B for beta-cell function, utilizes fasting plasma glucose and insulin concentrations to provide an estimate of these parameters^[6]. Another significant measure is the Insulin Sensitivity Index (ISI), such as ISI<, which evaluates the body’s response to insulin over time, often after a glucose challenge^[7]. These indices are vital for both clinical diagnosis and research into diabetes-related traits and pathways ^[8].

Key Variants

RS ID	Gene	Related Traits
rs11603334 rs57614870	ARAP1	insulin measurement blood glucose amount blood glucose amount, body mass index body mass index HbA1c measurement
rs10501320 rs11039182 rs7944584	MADD	insulin measurement nervousness feeling nervous measurement glucose measurement Alzheimer disease, blood glucose amount
rs7903146 rs35198068 rs4575195	TCF7L2	insulin measurement clinical laboratory measurement, glucose measurement body mass index type 2 diabetes mellitus type 2 diabetes mellitus, metabolic syndrome
rs9273368	HLA-DQA1 - HLA-DQB1	type 2 diabetes mellitus latent autoimmune diabetes in adults, type 2 diabetes mellitus type 1 diabetes mellitus, latent autoimmune diabetes in adults HLA class II histocompatibility antigen gamma chain measurement level of complement C1q subcomponent subunit A in blood
rs2943646 rs2943641	NYAP2 - MIR5702	systolic blood pressure insulin measurement high density lipoprotein cholesterol measurement triglyceride measurement phospholipids in HDL measurement
rs1260326	GCKR	urate measurement total blood protein measurement serum albumin amount coronary artery calcification lipid measurement
rs11020114	SNRPGP16 - MTNR1B	insulin measurement
rs13169290	CAST, PCSK1	protein measurement insulin measurement neuroendocrine convertase 1 measurement smoking initiation glucose measurement
rs7012814 rs7012637	PPP1R3B-DT	circulating fibrinogen levels glomerular filtration rate insulin measurement serum gamma-glutamyl transferase measurement BMI-adjusted waist circumference
rs13389219 rs10195252 rs1128249	COBLL1	reticulocyte count waist-hip ratio insulin measurement serum alanine aminotransferase amount calcium measurement

Methods and Criteria for Insulin Assessment

Accurate assessment of insulin levels relies on precise measurement approaches and strict diagnostic criteria. The most common approach involves measuringfasting plasma insulin (FPI), where blood samples are collected after a period of overnight fasting ^[1]. Other measurements, such as 2-hour insulinlevels, are obtained after an oral glucose tolerance test to assess post-prandial insulin response^[1]. For reliable data, individuals are typically excluded from analyses if their blood sample was non-fasting, if they are diabetic, on diabetic medication, or pregnant ^[3].

Beyond the direct measurement of insulin, various factors influence its levels and must be considered, often through statistical adjustments. These include age, sex, body mass index (BMI), smoking status, menopausal status, and hormone therapy use^[9]. Such adjustments are crucial for standardizing research criteria and ensuring that observed associations reflect underlying biological mechanisms rather than confounding demographic or lifestyle variables. Insulin levels, along with glucose concentrations, serve as keybiomarkers for monitoring metabolic health and predicting the risk of conditions like type 2 diabetes ^[8]. For association analyses, insulin levels are often natural log transformed to normalize their distribution^[3].

Classification of Insulin-Related States and Terminology

The classification of insulin-related states often navigates between categorical diagnoses and dimensional approaches. While conditions like type 2 diabetes are clinically defined, the underlying physiological dysfunctions, including insulin resistance, are recognized as existing on a continuum^[10]. Type 2 diabetes itself is understood as a heterogeneous disease stemming from various physiological dysfunctions across different tissues, including the pancreas, skeletal muscle, liver, adipose, and vascular tissue^[2]. This heterogeneity underscores the importance of precise insulin assessment in dissecting the complex genetic architecture and progression of the disease.

Terminology in this field includes diabetes-related quantitative traits, which encompass measurable characteristics like fasting glucose and insulin levels that are continuously variable and associated with diabetes risk^[2]. These are often considered intermediate phenotypes, providing a more detailed view of potentially affected metabolic pathways compared to binary disease outcomes^[1]. The recognition that metabolic risk factors, such as insulin resistance, worsen continuously across the spectrum of non-diabetic glucose tolerance supports a dimensional perspective, allowing for a more nuanced understanding of disease progression and personalized health care approaches^[10].

Diagnosis of Insulin Levels

Assessing insulin levels is a critical component in the diagnosis and management of various metabolic conditions, particularly those related to glucose homeostasis and energy metabolism. The diagnostic approach integrates clinical findings with advanced biochemical and genetic analyses to provide a comprehensive understanding of an individual’s metabolic state.

Clinical Evaluation and Phenotypic Assessment

The initial diagnostic phase for conditions involving insulin often begins with a thorough clinical evaluation, which includes assessing an individual’s medical history, lifestyle factors, and physical examination. Key factors considered during this assessment include age, smoking status, body-mass index (BMI), hormone-therapy use, and menopausal status, as these can significantly influence metabolic health

The pancreas, specifically its beta-cells, is responsible for producing and secreting insulin in response to elevated blood glucose. Once secreted, insulin orchestrates a complex network of molecular and cellular pathways. These pathways involve the binding of insulin to its specific receptors on target cells, triggering a cascade of intracellular signals that lead to increased glucose transport into cells and the synthesis of glycogen in the liver and muscles, as well as fatty acids in adipose tissue^[2]

Tissue-Specific Actions and Inter-Organ Communication

Insulin exerts its effects across multiple tissues and organs, collectively regulating systemic metabolism. The liver, skeletal muscle, and adipose tissue are primary targets, each contributing uniquely to glucose and energy homeostasis. In the liver, insulin promotes glucose storage as glycogen and inhibits glucose production, while in skeletal muscle, it stimulates glucose uptake for energy and glycogen synthesis^[2]

Adipose tissue responds to insulin by enhancing glucose uptake and promoting the storage of triglycerides, a crucial energy reserve. Dysfunction in any of these tissues can lead to systemic imbalances, such as insulin resistance, where cells fail to respond adequately to insulin’s signals. This inter-organ communication and coordinated response are vital for overall metabolic health, and their disruption is central to the development of diseases like type 2 diabetes^[2]

Genetic Underpinnings of Insulin Regulation and Related Traits

Genetic mechanisms play a substantial role in the individual variability observed in insulin levels and related metabolic traits, influencing susceptibility to conditions like type 2 diabetes. Genome-wide association studies (GWAS) have been instrumental in identifying specific genetic loci associated with diabetes-related traits, including type 2 diabetes and triglyceride levels, highlighting the polygenic nature of these conditions. These studies provide insights into the complex genetic architecture underlying metabolic dysfunction^[2]

For instance, common genetic variants in genes such as Hexokinase 1 (HK1) have been associated with glycated hemoglobin levels in non-diabetic populations. Such genetic associations suggest that variations in gene function or expression patterns, potentially mediated by regulatory elements, can influence key metabolic processes. Understanding these genetic contributions helps to delineate potentially affected pathways and move towards personalized health care approaches^[11]

Pathophysiological Processes and Disease Development

Pathophysiological processes underlying metabolic disorders often involve a breakdown in the homeostatic regulation of insulin. Insulin resistance, a condition where peripheral tissues exhibit a reduced response to insulin, is a critical early event in the progression towards type 2 diabetes. This resistance frequently leads to compensatory responses from the pancreas, which increases insulin production to maintain normal glucose levels^[2]

Over time, the sustained demand on pancreatic beta-cells can lead to their dysfunction and eventual failure, resulting in insufficient insulin production and overt hyperglycemia, characteristic of type 2 diabetes. This heterogeneous disease arises from physiological dysfunction spanning the pancreas, skeletal muscle, liver, adipose, and vascular tissues. A deeper understanding of these disease mechanisms, informed by genetic and metabolomic characterization, is crucial for prevention and control^[2]

Pathways and Mechanisms

Genetic Influences on Metabolic Regulation

Genetic variations, such as single nucleotide polymorphisms (SNPs), can profoundly influence the regulation of metabolic pathways involved in insulin action. These genetic differences impact various regulatory mechanisms, including gene expression and protein modification. For instance, common SNPs in the HMGCR gene, which is central to cholesterol biosynthesis, have been shown to affect alternative splicing of exon 13, thereby altering protein function and subsequent LDL-cholesterol levels^[5]. Similarly, variations in the HK1 gene, encoding Hexokinase 1, have been associated with glycated hemoglobin levels in non-diabetic populations, highlighting the genetic control over key enzymes in glucose metabolism^[11]. Such genetic insights provide detailed information on potentially affected pathways by linking specific genotypes to intermediate phenotypes, paving the way for personalized health care based on an individual’s genetic and metabolic profile ^[1].

Insulin’s Central Role in Energy Metabolism and Homeostasis

Insulin plays a fundamental role in orchestrating energy metabolism, governing the balance between biosynthesis and catabolism of key macromolecules. Its action ensures the efficient uptake and utilization of glucose by cells, promoting energy storage and preventing hyperglycemia. This intricate regulation of metabolic pathways by insulin is crucial for maintaining overall metabolic homeostasis, influencing various metabolite profiles in human serum^[1]. The efficacy of insulin in managing these metabolic fluxes is central to systemic health, with dysregulation in these processes leading to significant shifts in glucose and lipid metabolism, which are assessed through diabetes-related traits and measures of insulin resistance^[2].

Interconnectedness of Insulin Action and Broader Physiological Networks

The pathways influenced by insulin do not operate in isolation but are intricately woven into a complex network of physiological interactions. This systems-level integration involves significant pathway crosstalk, where the regulation of one metabolic process, such as glucose utilization, directly impacts others, like lipid synthesis and breakdown^[12]. Genetic studies reveal that common variants across numerous loci contribute to complex metabolic phenotypes, such as polygenic dyslipidemia, indicating a hierarchical regulation where multiple genetic factors collectively influence emergent properties of metabolic health ^[13]. Understanding these network interactions and the interplay of various intermediate phenotypes provides a more detailed view of potentially affected pathways, moving beyond single-gene effects to a more holistic understanding of metabolic function ^[1].

Dysregulation of Insulin Pathways in Disease

Dysregulation of insulin pathways is a primary mechanism underlying numerous metabolic diseases, most notably type 2 diabetes and various forms of dyslipidemia. Insulin resistance, a condition where target cells fail to respond adequately to insulin’s signals, represents a key pathway dysregulation^[2]. In response to this resistance, pancreatic beta-cells may exhibit compensatory mechanisms by increasing insulin production; however, their function can eventually decline, leading to insufficient insulin levels^[2]. The assessment of traits like glycated hemoglobin, insulin resistance, and beta-cell function from fasting plasma glucose and insulin concentrations serves as crucial indicators of these disease-relevant mechanisms, offering insights into potential therapeutic targets for managing metabolic disorders^[2].

Clinical Relevance

Diagnostic Utility and Risk Stratification for Metabolic Disorders

Insulin levels, including fasting insulin and 2-hour insulin following a glucose challenge, are crucial for evaluating insulin resistance and beta-cell function. Indices such as the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), derived from fasting plasma glucose and insulin concentrations, and the insulin sensitivity index serve as valuable tools for this assessment^[6]. These tools are instrumental in the early identification of individuals at high risk for developing type 2 diabetes, enabling targeted prevention strategies. Research indicates that simple assessments of insulin resistance can effectively predict type 2 diabetes, thereby facilitating personalized medical approaches^[8].

Integrating metabolic characterization, which includes individual insulin profiles, with genetic information offers a pathway towards personalized healthcare and nutrition^[1]. This comprehensive approach allows for a more detailed understanding of potentially affected metabolic pathways and the identification of intermediate phenotypes on a continuous scale ^[1]. Such detailed stratification is essential for tailoring interventions to individuals based on their unique metabolic and genetic predispositions, potentially improving health outcomes before the onset of full-blown disease.

Prognostic Indicator for Diabetes and Cardiovascular Complications

Beyond diagnosis, insulin levels and related indices possess significant prognostic value for disease progression and the prediction of long-term health outcomes. Elevated fasting insulin, for instance, has been independently associated with an increased risk of stroke and coronary heart disease^[14]. This highlights its role not only in the context of diabetes but also as a critical predictor for broader cardiovascular morbidity. Understanding these prognostic implications allows for earlier and more aggressive management of at-risk individuals.

For individuals already diagnosed with diabetes, particularly type 1, intensive treatment focused on blood glucose control, often involving insulin therapy, has been shown to reduce the development and progression of long-term complications^[15]. Similarly, in type 2 diabetes, intensive blood-glucose control with insulin or sulphonylureas significantly impacts the risk of complications^[16]. This underscores the importance of monitoring insulin dynamics in guiding therapeutic strategies aimed at mitigating adverse long-term implications and improving patient longevity.

Insights into Comorbidities and Therapeutic Guidance

Insulin resistance is frequently intertwined with a spectrum of comorbidities, forming overlapping phenotypes such as polygenic dyslipidemia and metabolic syndrome^[17]. Common genetic variants have been identified that contribute to dyslipidemia, a condition often associated with abnormal insulin sensitivity^[17]. Furthermore, metabolic syndrome pathways, including those involving LEPR, HNF1A, IL6R, and GCKR, are linked to systemic inflammation, as indicated by plasma C-reactive protein levels, reinforcing the systemic impact of insulin dysregulation^[4].

Understanding these complex associations is vital for comprehensive patient care, allowing clinicians to anticipate and manage a broader range of complications, including subclinical atherosclerosis^[18]. Monitoring insulin dynamics can thus inform treatment selection, not just for glycemic control but also for managing related conditions. For instance, the use of insulin in intensive blood-glucose control regimens in type 2 diabetes aims to reduce the risk of complications, demonstrating its direct application in therapeutic decision-making^[16].

Frequently Asked Questions About Insulin Measurement

These questions address the most important and specific aspects of insulin measurement based on current genetic research.

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1. Why does my blood sugar spike while my friend eats the same?

Your body’s response to food, including insulin secretion and glucose uptake, can be significantly influenced by your unique genetic makeup. While diet plays a huge role, variations in genes affecting glucose metabolism or insulin sensitivity mean some people process sugars differently. This could lead to different blood sugar spikes even when eating the same meal.

2. Does stress really make my body handle insulin worse?

Yes, stress can indeed impact your body’s insulin sensitivity and glucose regulation. While not directly detailed as a genetic variant, environmental factors like stress are known to influence metabolic health. Chronic stress can lead to hormonal changes that make your cells less responsive to insulin, potentially causing higher blood sugar levels.

3. I eat healthy, but will my family’s diabetes history catch up?

Your family history indicates a genetic predisposition, meaning you might have variants in genes like TCF7L2 or GCKR that increase your risk for insulin dysregulation. While healthy eating is crucial, these genetic factors can influence how efficiently your body produces or uses insulin. However, lifestyle choices are powerful and can often mitigate or delay the onset of such conditions.

4. Does my ethnic background change my risk for insulin problems?

Yes, genetic studies have shown that metabolic traits, including insulin regulation, can vary significantly across different ancestral groups. Research often identifies specific genetic architectures within certain populations, such as those from Finland or Micronesia. This means your ethnic background might influence your unique genetic risk factors for insulin-related conditions.

5. Why do some people naturally have better blood sugar control than me?

Individual differences in blood sugar control are often rooted in genetics, influencing how your pancreas produces insulin and how your cells respond to it. Some people inherit gene variants that lead to more efficient insulin secretion or better glucose uptake, giving them a natural advantage. These genetic differences mean some bodies are simply better at maintaining stable blood sugar.

6. Can I predict if I’ll have insulin resistance later in life?

Genetic research, particularly using genome-wide association studies, is identifying markers that predispose individuals to insulin dysregulation, including insulin resistance. By analyzing specific genetic variants, it’s becoming possible to assess your risk profile. While not a definitive prediction, these insights can inform proactive interventions and personalized strategies to manage your metabolic health.

7. Does getting older make my body less efficient with insulin?

Yes, age is a significant factor that can influence your body’s insulin efficiency. As you get older, physiological changes can occur that affect insulin production and sensitivity, making it a crucial confounder in metabolic studies. This means your body might naturally become less effective at regulating glucose as you age, regardless of genetic predispositions.

8. Why are my insulin levels so sensitive to my daily habits?

Your insulin levels are highly susceptible to various physiological and environmental factors, including your daily habits like diet, exercise, and sleep. Studies strictly control for factors like fasting status, as even minor deviations can significantly alter results. This phenotypic complexity means your day-to-day choices have a direct and measurable impact on your body’s insulin response.

9. My sibling is thin but I’m not; is my insulin different?

Even within families, genetic predispositions for insulin regulation and metabolic traits can manifest differently due to varying combinations of inherited gene variants. While you share many genes with your sibling, subtle differences in variants influencing insulin secretion or action could lead to differing metabolic profiles. Environmental factors and lifestyle choices also play a significant role in these individual outcomes.

10. Can regular exercise truly overcome my genetic predisposition to high insulin?

While genetic variants can predispose you to higher insulin levels or insulin resistance, lifestyle factors like regular exercise are incredibly powerful. Exercise improves insulin sensitivity, helping your cells better utilize glucose. Though genetics set a baseline, proactive interventions like physical activity can significantly mitigate inherited risks and positively influence your metabolic health.

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This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

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[2] Meigs, J. B. “Genome-Wide Association with Diabetes-Related Traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. 56.

[3] Sabatti, C. et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2009.

[4] Ridker, P. M. et al. “Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”Am J Hum Genet, 2008.

[5] Burkhardt, R., et al. “Common SNPs in HMGCR in Micronesians and Whites Associated with LDL-Cholesterol Levels Affect Alternative Splicing of Exon13.” Arterioscler Thromb Vasc Biol, 2008, PMID: 18802019.

[6] Matthews, D. R. et al. “Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.”Diabetologia, 1985.

[7] Gutt, M. et al. “Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures.”Diabetes Res Clin Pract, 2000.

[8] Hanley, A. J. et al. “Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study.”Diabetes, 2003.

[9] Hwang, S. J. et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, 2007.

[10] Meigs, J. B. et al. “Metabolic risk factors worsen continuously across the spectrum of nondiabetic glucose tolerance: the Framingham Offspring Study.”Annals of Internal Medicine, 1998.

[11] Pare, G. “Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women’s Genome Health Study.”PLoS Genet, vol. 4, no. 12, 2008, e1000312.

[12] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-169.

[13] Kathiresan, Sekar, et al. “Common Variants at 30 Loci Contribute to Polygenic Dyslipidemia.” Nature Genetics, vol. 41, no. 1, 2009, pp. 56-65.

[14] Lawlor, D. A., et al. “Independent Associations of Fasting Insulin, Glucose, and Glycated Haemoglobin with Stroke and Coronary Heart Disease.”Intern Med, vol. 110, 2007, pp. 125-137.

[15] The Diabetes Control and Complications Trial Research Group. “The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus.”New England Journal of Medicine, vol. 329, 1993, pp. 977-986.

[16] UK Prospective Diabetes Study Group. “Intensive Blood-Glucose Control with Sulphonylureas or Insulin Compared with Conventional Treatment and Risk of Complications in Patients with Type 2 Diabetes (UKPDS 33).”Lancet, vol. 352, 1998, pp. 837-853.

[17] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 12, 2008, pp. 1493-1498.

[18] O’Donnell, Christopher J., et al. “Genome-Wide Association Study for Subclinical Atherosclerosis in Major Arterial Territories in the NHLBI’s Framingham Heart Study.”BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S4.

[19] Grant, S. F., et al. “Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes.” Nat Genet, vol. 38, no. 3, 2006, pp. 320-323.

[20] Melzer, David, et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.

[21] Ober, Carole, et al. “Genome-wide association study of plasma lipoprotein(a) levels identifies multiple genes on chromosome 6q.”J Lipid Res, vol. 50, no. 4, 2009, pp. 748-755.

[22] Rutter, M. K. et al. “Insulin Resistance, the Metabolic Syndrome, and Incident Cardiovascular Events in The Framingham Offspring Study.”Diabetes, 2005.

[23] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 40, no. 12, 2008, pp. 1394-403.

[24] Wallace, C., et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 139-149.